Paper2Agent is a multi-agent AI system that automatically transforms research papers into interactive AI agents with minimal human input. Here are some Demos of the Paper2Agent-generated agent.
Automatically detects and runs all relevant tutorials from a research paper’s codebase.
⚠️ Prerequisites: Complete the installation & setup below before running Paper2Agent.⏱️ Runtime & Cost: Processing time varies from 30 minutes to 3+ hours based on codebase complexity. Estimated cost: ~$15 for complex repositories like AlphaGenome using Claude Sonnet 4 (one-time cost).
cd Paper2Agent
bash Paper2Agent.sh \
--project_dir <PROJECT_DIR> \
--github_url <GITHUB_URL>Process only specific tutorials by title or URL:
bash Paper2Agent.sh \
--project_dir <PROJECT_DIR> \
--github_url <GITHUB_URL> \
--tutorials <TUTORIALS_URL or TUTORIALS_TITLE>For repositories requiring authentication:
bash Paper2Agent.sh \
--project_dir <PROJECT_DIR> \
--github_url <GITHUB_URL> \
--api <API_KEY>Required:
--project_dir <directory>: Name of the project directory to create- Example:
TISSUE_Agent
- Example:
--github_url <url>: GitHub repository URL to analyze- Example:
https://github.com/sunericd/TISSUE
- Example:
Optional:
--tutorials <filter>: Filter tutorials by title or URL- Example:
"Preprocessing and clustering"or tutorial URL
- Example:
--api <key>: API key for repositories requiring authentication- Example:
your_api_key_here
- Example:
--benchmark: Run benchmark extraction and assessment (default: disabled)
Create an AI agent from the TISSUE research paper codebase for uncertainty-calibrated single-cell spatial transcriptomics analysis:
bash Paper2Agent.sh \
--project_dir TISSUE_Agent \
--github_url https://github.com/sunericd/TISSUECreate an AI agent from the Scanpy research paper codebase for single-cell analysis preprocessing and clustering:
# Filter by tutorial title
bash Paper2Agent.sh \
--project_dir Scanpy_Agent \
--github_url https://github.com/scverse/scanpy \
--tutorials "Preprocessing and clustering"
# Filter by tutorial URL
bash Paper2Agent.sh \
--project_dir Scanpy_Agent \
--github_url https://github.com/scverse/scanpy \
--tutorials "https://github.com/scverse/scanpy/blob/main/docs/tutorials/basics/clustering.ipynb"Create an AI agent from the AlphaGenome research paper codebase for genomic data interpretation:
bash Paper2Agent.sh \
--project_dir AlphaGenome_Agent \
--github_url https://github.com/google-deepmind/alphagenome \
--api <ALPHAGENOME_API_KEY>- Python: Version 3.10 or higher
- Claude Code: Install following instructions at anthropic.com/claude-code
-
Clone the Paper2Agent Repository
git clone https://github.com/jmiao24/Paper2Agent.git cd Paper2Agent -
Install Python Dependencies
pip install fastmcp
-
Install and Configure Claude Code
npm install -g @anthropic-ai/claude-code claude
To streamline usage, we recommend creating Paper Agents by connecting Paper MCP servers to an AI coding agent, such as Claude Code or the Google Gemini CLI (it's free with a Google account!). We are also actively developing our own base agent, which will be released soon.
After pipeline completion, Claude Code will automatically open with your new MCP server loaded.
To restart your agent later:
cd <working_dir>
fastmcp install claude-code <project_dir>/src/<repo_name>_mcp.py \
--python <project_dir>/<repo_name>-env/bin/pythonTo create a paper agent in Claude Code with the Paper MCP server of interest, use the following script with your own working directory, MCP name, and server URL:
bash launch_remote_mcp.sh \
--working_dir <working_dir> \
--mcp_name <mcp_name> \
--mcp_url <remote_mcp_url>For example, to create an AlphaGenome Agent, run:
bash launch_remote_mcp.sh \
--working_dir analysis_dir \
--mcp_name alphagenome \
--mcp_url https://Paper2Agent-alphagenome-mcp.hf.space✅ You will now have an AlphaGenome Agent ready for genomics data interpretation. You can input the query like:
Analyze heart gene expression data with AlphaGenome MCP to identify the causal gene
for the variant chr11:116837649:T>G, associated with Hypoalphalipoproteinemia.
To reuse the AlphaGenome agent, run
cd analysis_dir
claudeVerify your agent is loaded:
claude mcp listor use \mcp inside Claude Code. You should see your repository-specific MCP server listed.
After completion, your project will contain:
<project_dir>/
├── src/
│ ├── <repo_name>_mcp.py # Generated MCP server
│ └── tools/
│ └── <tutorial_file_name>.py # Extracted tools from each tutorial
├── <repo_name>-env/ # Isolated Python environment
├── repo/
│ └── <repo_name>/ # Cloned repository with original code
├── claude_outputs/
│ ├── step1_output.json # Tutorial scanner results
│ ├── step2_output.json # Tutorial executor results
│ ├── step3_output.json # Tool extraction results
│ ├── step4_output.json # MCP server creation results
│ └── step5_output.json # Coverage and quality analysis results
├── reports/
│ ├── tutorial-scanner.json # Tutorial discovery analysis
│ ├── tutorial-scanner-include-in-tools.json # Tools inclusion decisions
│ ├── executed_notebooks.json # Notebook execution summary
│ ├── environment-manager_results.md # Environment setup details
│ ├── coverage/ # Code coverage analysis reports
│ │ ├── coverage.xml # XML coverage report (CI/CD format)
│ │ ├── coverage.json # JSON coverage report (machine-readable)
│ │ ├── coverage_summary.txt # Text summary of coverage metrics
│ │ ├── coverage_report.md # Detailed markdown coverage analysis
│ │ ├── pytest_output.txt # Full pytest execution output
│ │ └── htmlcov/ # HTML coverage dashboard (interactive)
│ ├── quality/ # Code quality analysis reports
│ │ └── pylint/ # Pylint code style analysis
│ │ ├── pylint_report.txt # Full pylint analysis output
│ │ ├── pylint_scores.txt # Per-file pylint scores summary
│ │ └── pylint_issues.md # Detailed style issues breakdown
│ └── coverage_and_quality_report.md # Combined coverage + quality report
├── tests/
│ ├── code/<tutorial_file_name>/ # Test code for extracted tools
│ ├── data/<tutorial_file_name>/ # Test data files
│ ├── results/<tutorial_file_name>/ # Test execution results
│ └── logs/ # Test execution logs
├── notebooks/
│ └── <tutorial_file_name>/
│ ├── <tutorial_file_name>_execution_final.ipynb # Executed tutorial
│ └── images/ # Generated plots and visualizations
└── tools/ # Additional utility scripts
| File/Directory | Description |
|---|---|
src/<repo_name>_mcp.py |
Main MCP server file that Claude Code loads |
src/tools/<tutorial_file_name>.py |
Individual tool modules extracted from each tutorial |
<repo_name>-env/ |
Isolated Python environment with all dependencies |
reports/coverage/ |
Code coverage analysis reports (pytest-cov) |
reports/quality/pylint/ |
Code style analysis reports (pylint) |
reports/coverage_and_quality_report.md |
Combined coverage + quality metrics report |
reports/benchmark_questions.csv |
(Optional) Benchmark questions extracted from the executed tutorials (if --benchmark used) |
reports/benchmark_results.csv |
(Optional) Benchmark assessment results of the final agent and MCP tools (if --benchmark used) |
Below, we showcase demos of AI agents created by Paper2Agent, illustrating how each agent applies the tools from its source paper to tackle scientific tasks.
Example query:
Analyze heart gene expression data with AlphaGenome MCP to identify the causal gene
for the variant chr11:116837649:T>G, associated with Hypoalphalipoproteinemia.
AlphaGenome_chatbot.mov
Example query:
Calculate the 95% prediction interval for the spatial gene expression prediction of gene Acta2 using TISSUE MCP.
This is my data:
Spatial count matrix: Spatial_count.txt
Spatial locations: Locations.txt
scRNA-seq count matrix: scRNA_count.txt
TISSUE_chatbot.mov
Example query:
Use Scanpy MCP to preprocess and cluster the single-cell dataset pbmc_all.h5ad.
- AlphaGenome: https://Paper2Agent-alphagenome-mcp.hf.space
- Scanpy: https://Paper2Agent-scanpy-mcp.hf.space
- TISSUE: https://Paper2Agent-tissue-mcp.hf.space
For comprehensive benchmarking results and evaluation metrics of the AlphaGenome Agent, please refer to our dedicated benchmarking repository: Paper2AgentBench. This repository contains our benchmarking tools and evaluation metrics for the AlphaGenome Agent compared to the Claude + Repo and Biomni agents.
@misc{miao2025paper2agent,
title={Paper2Agent: Reimagining Research Papers As Interactive and Reliable AI Agents},
author={Jiacheng Miao and Joe R. Davis and Jonathan K. Pritchard and James Zou},
year={2025},
eprint={2509.06917},
archivePrefix={arXiv},
primaryClass={cs.AI},
url={https://arxiv.org/abs/2509.06917},
}